Update README.md

README.md

@@ -1,3 +1,214 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+### Label info:
+```
+0: "fragment",
+1: "statement",
+2: "question",
+3: "command",
+4: "rhetorical question",
+5: "rhetorical command",
+6: "intonation-dependent utterance"
+```
+
+### Training process:
+```
+{'loss': 1.8008, 'grad_norm': 7.2770233154296875, 'learning_rate': 1e-05, 'epoch': 0.03}
+{'loss': 0.894, 'grad_norm': 27.84651756286621, 'learning_rate': 2e-05, 'epoch': 0.06}
+{'loss': 0.6504, 'grad_norm': 30.617990493774414, 'learning_rate': 3e-05, 'epoch': 0.09}
+{'loss': 0.5939, 'grad_norm': 34.73934555053711, 'learning_rate': 4e-05, 'epoch': 0.12}
+{'loss': 0.5786, 'grad_norm': 6.585583209991455, 'learning_rate': 5e-05, 'epoch': 0.15}
+{'eval_loss': 0.5915874242782593, 'eval_accuracy': 0.8297766749379653, 'eval_f1': 0.8315132136625163, 'eval_precision': 0.8410462605264737, 'eval_recall': 0.8297766749379653, 'eval_runtime': 265.1144, 'eval_samples_per_second': 22.801, 'eval_steps_per_second': 1.426, 'epoch': 0.15}
+{'loss': 0.5928, 'grad_norm': 10.66515064239502, 'learning_rate': 4.8276456394346784e-05, 'epoch': 0.18}
+{'loss': 0.5611, 'grad_norm': 3.804234266281128, 'learning_rate': 4.655291278869355e-05, 'epoch': 0.21}
+{'loss': 0.5151, 'grad_norm': 8.275078773498535, 'learning_rate': 4.4829369183040333e-05, 'epoch': 0.24}
+{'loss': 0.4696, 'grad_norm': 2.44854474067688, 'learning_rate': 4.310582557738711e-05, 'epoch': 0.26}
+{'loss': 0.5183, 'grad_norm': 8.534456253051758, 'learning_rate': 4.138228197173389e-05, 'epoch': 0.29}
+{'eval_loss': 0.5429911017417908, 'eval_accuracy': 0.8415219189412738, 'eval_f1': 0.8231674368620022, 'eval_precision': 0.8383674385161947, 'eval_recall': 0.8415219189412738, 'eval_runtime': 268.1016, 'eval_samples_per_second': 22.547, 'eval_steps_per_second': 1.41, 'epoch': 0.29}
+{'loss': 0.4802, 'grad_norm': 10.636425018310547, 'learning_rate': 3.965873836608066e-05, 'epoch': 0.32}
+{'loss': 0.4877, 'grad_norm': 6.05213737487793, 'learning_rate': 3.793519476042744e-05, 'epoch': 0.35}
+{'loss': 0.5093, 'grad_norm': 5.5984015464782715, 'learning_rate': 3.621165115477422e-05, 'epoch': 0.38}
+{'loss': 0.496, 'grad_norm': 7.945780277252197, 'learning_rate': 3.4488107549120996e-05, 'epoch': 0.41}
+{'loss': 0.5005, 'grad_norm': 5.778200626373291, 'learning_rate': 3.276456394346777e-05, 'epoch': 0.44}
+{'eval_loss': 0.41184064745903015, 'eval_accuracy': 0.8684863523573201, 'eval_f1': 0.8635611747282996, 'eval_precision': 0.8629771033516368, 'eval_recall': 0.8684863523573201, 'eval_runtime': 270.0108, 'eval_samples_per_second': 22.388, 'eval_steps_per_second': 1.4, 'epoch': 0.44}
+{'loss': 0.4436, 'grad_norm': 4.413114070892334, 'learning_rate': 3.1041020337814545e-05, 'epoch': 0.47}
+{'loss': 0.4899, 'grad_norm': 18.563016891479492, 'learning_rate': 2.9317476732161327e-05, 'epoch': 0.5}
+{'loss': 0.4637, 'grad_norm': 26.92985725402832, 'learning_rate': 2.7593933126508105e-05, 'epoch': 0.53}
+{'loss': 0.4387, 'grad_norm': 7.494612693786621, 'learning_rate': 2.5870389520854876e-05, 'epoch': 0.56}
+{'loss': 0.4401, 'grad_norm': 20.5152530670166, 'learning_rate': 2.4146845915201654e-05, 'epoch': 0.59}
+{'eval_loss': 0.42229706048965454, 'eval_accuracy': 0.8663358147229115, 'eval_f1': 0.859666580414163, 'eval_precision': 0.8638930298685418, 'eval_recall': 0.8663358147229115, 'eval_runtime': 272.7465, 'eval_samples_per_second': 22.163, 'eval_steps_per_second': 1.386, 'epoch': 0.59}
+{'loss': 0.4289, 'grad_norm': 10.1361665725708, 'learning_rate': 2.2423302309548433e-05, 'epoch': 0.62}
+{'loss': 0.4193, 'grad_norm': 8.068666458129883, 'learning_rate': 2.0699758703895207e-05, 'epoch': 0.65}
+{'loss': 0.4038, 'grad_norm': 8.713869094848633, 'learning_rate': 1.8976215098241985e-05, 'epoch': 0.68}
+{'loss': 0.4073, 'grad_norm': 12.182595252990723, 'learning_rate': 1.7252671492588764e-05, 'epoch': 0.71}
+{'loss': 0.4095, 'grad_norm': 13.43953800201416, 'learning_rate': 1.5529127886935542e-05, 'epoch': 0.74}
+{'eval_loss': 0.3974127173423767, 'eval_accuracy': 0.8726220016542597, 'eval_f1': 0.8677290061110087, 'eval_precision': 0.8672987137526573, 'eval_recall': 0.8726220016542597, 'eval_runtime': 270.2975, 'eval_samples_per_second': 22.364, 'eval_steps_per_second': 1.398, 'epoch': 0.74}
+{'loss': 0.3473, 'grad_norm': 16.423139572143555, 'learning_rate': 1.3805584281282317e-05, 'epoch': 0.76}
+{'loss': 0.3982, 'grad_norm': 6.357703685760498, 'learning_rate': 1.2082040675629095e-05, 'epoch': 0.79}
+{'loss': 0.3286, 'grad_norm': 4.977189064025879, 'learning_rate': 1.0358497069975871e-05, 'epoch': 0.82}
+{'loss': 0.3712, 'grad_norm': 4.068944454193115, 'learning_rate': 8.634953464322648e-06, 'epoch': 0.85}
+{'loss': 0.345, 'grad_norm': 6.266202926635742, 'learning_rate': 6.911409858669425e-06, 'epoch': 0.88}
+{'eval_loss': 0.3740645945072174, 'eval_accuracy': 0.8822167080231597, 'eval_f1': 0.8780706451391699, 'eval_precision': 0.877925468669178, 'eval_recall': 0.8822167080231597, 'eval_runtime': 270.0795, 'eval_samples_per_second': 22.382, 'eval_steps_per_second': 1.4, 'epoch': 0.88}
+{'loss': 0.4049, 'grad_norm': 10.76927375793457, 'learning_rate': 5.187866253016201e-06, 'epoch': 0.91}
+{'loss': 0.3919, 'grad_norm': 12.331282615661621, 'learning_rate': 3.4643226473629783e-06, 'epoch': 0.94}
+{'loss': 0.3576, 'grad_norm': 8.6154203414917, 'learning_rate': 1.7407790417097554e-06, 'epoch': 0.97}
+{'loss': 0.3544, 'grad_norm': 10.01504135131836, 'learning_rate': 1.723543605653223e-08, 'epoch': 1.0}
+{'train_runtime': 7076.4012, 'train_samples_per_second': 7.688, 'train_steps_per_second': 0.481, 'train_loss': 0.5087223172678522, 'epoch': 1.0}
+100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3401/3401 [1:57:56<00:00, 2.08s/it]
+Training completed. Model saved.
+```
+
+### Classification Report:
+```
+                                precision    recall  f1-score   support
+
+                      fragment       0.95      0.92      0.94       597
+                     statement       0.84      0.91      0.87      1811
+                      question       0.95      0.94      0.94      1786
+                       command       0.88      0.91      0.90      1296
+           rhetorical question       0.73      0.62      0.67       174
+            rhetorical command       0.86      0.56      0.68       108
+intonation-dependent utterance       0.57      0.38      0.46       273
+
+                      accuracy                           0.88      6045
+                     macro avg       0.83      0.75      0.78      6045
+                  weighted avg       0.88      0.88      0.88      6045
+
+Predictions saved
+```
+### Train code:
+```python
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from transformers import (
+    RobertaTokenizerFast, 
+    RobertaForSequenceClassification, 
+    Trainer, 
+    TrainingArguments,
+    EarlyStoppingCallback
+)
+from datasets import Dataset
+import torch
+import numpy as np
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report
+from tensorflow.python.keras.optimizer_v2.adam import Adam
+
+# Load and prepare data
+train_df = pd.read_csv("./train_fix_v1.csv")
+test_df = pd.read_csv("./test_fix_v1.csv")
+
+# Convert to Dataset objects
+train_dataset = Dataset.from_pandas(train_df)
+test_dataset = Dataset.from_pandas(test_df)
+
+# Initialize tokenizer and model
+model_name = "FacebookAI/roberta-base"
+tokenizer = RobertaTokenizerFast.from_pretrained(model_name)
+model = RobertaForSequenceClassification.from_pretrained(
+    model_name,
+    num_labels=7,
+    id2label={
+        0: "fragment",
+        1: "statement",
+        2: "question",
+        3: "command",
+        4: "rhetorical question",
+        5: "rhetorical command",
+        6: "intonation-dependent utterance"
+    },
+    label2id={
+        "fragment": 0,
+        "statement": 1,
+        "question": 2,
+        "command": 3,
+        "rhetorical question": 4,
+        "rhetorical command": 5,
+        "intonation-dependent utterance": 6
+    }
+)
+
+# Tokenize function
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
+
+# Tokenize datasets
+tokenized_train = train_dataset.map(tokenize_function, batched=True)
+tokenized_test = test_dataset.map(tokenize_function, batched=True)
+
+# Compute metrics function for evaluation
+def compute_metrics(pred):
+    labels = pred.label_ids
+    preds = pred.predictions.argmax(-1)
+    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')
+    acc = accuracy_score(labels, preds)
+    return {
+        'accuracy': acc,
+        'f1': f1,
+        'precision': precision,
+        'recall': recall
+    }
+
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="./roberta_base_stock",
+    num_train_epochs=1,  # Ustawione na 10, ale z early stopping
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    warmup_steps=500,
+    weight_decay=0.01,
+    logging_dir='./logs',
+    logging_steps=100,
+    evaluation_strategy="steps",
+    eval_steps=500,
+    save_strategy="steps",
+    save_steps=500,
+    load_best_model_at_end=True,
+    metric_for_best_model="f1",
+    learning_rate=5e-05,
+)
+
+# Initialize Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_train,
+    eval_dataset=tokenized_test,
+    compute_metrics=compute_metrics,
+    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
+)
+
+# Train the model
+trainer.train()
+
+# Save the fine-tuned model
+model.save_pretrained("./roberta_base_stock")
+tokenizer.save_pretrained("./roberta_base_stock")
+
+print("Training completed. Model saved.")
+
+# Evaluate the model on the test set
+print("Evaluating model on test set...")
+test_results = trainer.evaluate(tokenized_test)
+
+print("Test set evaluation results:")
+for key, value in test_results.items():
+    print(f"{key}: {value}")
+
+# Perform predictions on the test set
+test_predictions = trainer.predict(tokenized_test)
+
+# Get predicted labels
+predicted_labels = np.argmax(test_predictions.predictions, axis=1)
+true_labels = test_predictions.label_ids
+
+# Print classification report
+print("\nClassification Report:")
+print(classification_report(true_labels, predicted_labels, 
+                            target_names=model.config.id2label.values()))
+
+# Optional: Save predictions to CSV
+test_df['predicted_label'] = predicted_labels
+test_df.to_csv("./roberta_base_stock/test_predictions.csv", index=False)
+print("Predictions saved")
+```